计算机应用 ›› 2011, Vol. 31 ›› Issue (05): 1331-1334.DOI: 10.3724/SP.J.1087.2011.01331

• 人工智能 • 上一篇    下一篇

基于双重扰动的选择性支持向量机集成

陈涛   

  1. 陕西理工学院 数学系,陕西 汉中 723000
  • 收稿日期:2010-11-03 修回日期:2011-01-03 发布日期:2011-05-01 出版日期:2011-05-01
  • 通讯作者: 陈涛
  • 作者简介:陈涛(1979-),男,陕西汉中人,讲师,硕士,CCF会员,主要研究方向:支持向量机、机器学习。
  • 基金资助:

    国家自然科学基金资助项目(70472072);陕西省教育厅自然科学基金资助项目(09JK380);陕西理工学院自然基金资助项目(SLGKY10-20)。

Selective SVM ensemble based on double disturbance

CHEN Tao   

  1. Department of Mathematics, Shaanxi University of Technology, Hanzhong Shaanxi 723000, China
  • Received:2010-11-03 Revised:2011-01-03 Online:2011-05-01 Published:2011-05-01

摘要: 为了进一步提升支持向量机泛化性能,提出一种基于双重扰动的选择性支持向量机集成算法。利用Boosting方法对训练集进行扰动基础上,采用基于相对核的粗糙集相对约简与重采样技术相结合的动态约简算法进行特征扰动以生成个体成员,然后基于负相关学习理论构造遗传个体适应度函数,利用加速遗传算法选择权重大于阈值的最优个体进行加权集成。实验结果表明,该算法具有较高的泛化性能和较低的时、空复杂性,是一种高效的集成方法。

关键词: 扰动, 粗糙集, 相对核, 动态约简, 负相关学习, 加速遗传算法, 支持向量机集成

Abstract: This paper proposed a selective Support Vector Machine (SVM) ensemble algorithm based on double disturbance to improve the generalization ability of SVM. First, the training samples were disturbed by using conventional boosting algorithm, a dynamic reduction algorithm, which integrated relative reduction based on relative core of rough set and resample method, to produce individual SVM. The fitness function of genetic factors was established based on negative correlation learning, and Best SVM with weight larger than a given threshold value were selected by accelerating genetic algorithm and were integrated using weighted average. The experiments show that the algorithm has higher generalization performance, and lower time and space complexity. It is a highly effective ensemble algorithm.

Key words: disturbance, rough set, relative core, dynamic reduction, negative correlation learning, Accelerating Genetic Algorithm (AGA), support vector machine ensemble